Quantum marine predators algorithm for addressing multilevel image segmentation

Abd Elaziz, Mohamed; Mohammadi, Davood; Oliva, Diego; Salimifard, Khodakaram

Abstract

This paper proposes a modified marine predators algorithm based on quantum theory to handle the multilevel image segmentation problem. The main aims of using quantum theory is to enhance the ability of marine predators algorithm to find the optimal threshold levels to enhance the segmentation process. The proposed quantum marine predators algorithm gets the idea of finding a particle in the space based on a possible function borrowed from the Schrodinger wave function that determines the position of each particle at any time. This rectification in the search mechanism of the marine predators algorithm contributes to strengthening of exploration and exploitation of the algorithm. To analyze the performance of the proposed algorithm, we conduct a set of experiments. In the first experiment, the results of the developed quantum marine predators algorithm are compared with eight well-known meta-heuristics based on benchmark test functions. The second experiment demonstrates the applicability of the algorithm, in addressing multilevel threshold image segmentation. A set of ten gray-scale images assess the quality of the quantum marine predators algorithm and its performance is compared with other meta-heuristic algorithms. The experimental results show that the proposed algorithm performs well compared with other algorithms in terms of convergence and the quality of segmentation.

Citation

Abd Elaziz, M., Mohammadi, D., Oliva, D., & Salimifard, K. (2021). Quantum marine predators algorithm for addressing multilevel image segmentation. Applied Soft Computing, 110, Article 107598. https://doi.org/10.1016/j.asoc.2021.107598

• Journal Article Type: Article
• Acceptance Date: Jun 4, 2021
• Online Publication Date: Jun 11, 2021
• Publication Date: 2021-10
• Deposit Date: Feb 22, 2023
• Journal: Applied Soft Computing
• Print ISSN: 1568-4946
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 110
• Article Number: 107598
• DOI: https://doi.org/10.1016/j.asoc.2021.107598
• Public URL: https://hull-repository.worktribe.com/output/4210241